基于电子商务的精确搜索引擎的研究与实现

随着科技的发展,电子商务日益活跃。现行的电子商务打折网站与生俱来的局限性,越来越跟不上时代的步伐。能否开发一个能够收集全球打折信息的电子商务网站,越来越引起人们的重视。采用精确搜索引擎算法的电子商务精确搜索引擎模型,具有很好的精确性和响应速度,能够查询到精确而全面的打折信息,有较高的实用价值。     

基于图论的ANMP分群算法的简单实现

针对分布式无线网络多跳、移动的特点,要实现整个网络的快速部署以及拓扑结构变化后的动态重建,最有效的方法是采用分群协议,并在linux环境下,利用基于图论的ANMP分群算法,实现自组网的分群。     

Learning sorting algorithms through visualization construction

Recent increased interest in computational thinking poses an important question to researchers: What are the best ways to teach fundamental computing concepts to students? Visualization is suggested as one way of supporting student learning. This mixed-method study aimed to (i) examine the effect of instruction in which students constructed visualizations on students’ programming achievement and students’ attitudes toward computer programming, and (ii) explore how this kind of instruction supports students’ learning according to their self-reported experiences in the course. The study was conducted with 58 pre-service teachers who were enrolled in their second programming class. They expect to teach information technology and computing-related courses at the primary and secondary levels. An embedded experimental model was utilized as a research design. Students in the experimental group were given instruction that required students to construct visualizations related to sorting, whereas students in the control group viewed pre-made visualizations. After the instructional intervention, eight students from each group were selected for semi-structured interviews. The results showed that the intervention based on visualization construction resulted in significantly better acquisition of sorting concepts. However, there was no significant difference between the groups with respect to students’ attitudes toward computer programming. Qualitative data analysis indicated that students in the experimental group constructed necessary abstractions through their engagement in visualization construction activities. The authors of this study argue that the students’ active engagement in the visualization construction activities explains only one side of students’ success. The other side can be explained through the instructional approach, constructionism in this case, used to design instruction. The conclusions and implications of this study can be used by researchers and instructors dealing with computational thinking.     

高级语言教学如何取得良好的效果

高级语言教学应将算法设计、语言语法、程序运行环境等有机地结合起来,以取得良好的教学效果。     

遗传算法在股票投资技术分析中的应用

本文首先分析了股票投资技术分析的特点,然后阐述了遗传算法及基于遗传算法的分类器系统的基本理论.最后,详细讨论了遗传算法及分类器系统在两种最常用的股票投资技术分析方法(指标分析和图形分析)的计算机化中的运用问题.     

海量数据的MapReduce相似度检测

针对当前海量数据的相似重复问题,提出了MapReduce下通过SimHash算法检测相似文档的方法:即首先将存储在分布式文件系统的海量文档集进行分类,然后进行特征提取,由SimHash算法生成SimHash指纹和生成Sequence File;最后,计算相似度产生检测结果;通过实验测试可知,提出的检测方法和设计的相似度算法能很好适应海量数据相似检测,并能有效地提高工作效率。     

运筹学发展的回顾与展望

运筹学是20世纪三四十年代发展起来的一门新兴交叉学科,它主要研究如何应用数学和计算的理论与方法对社会系统和工程系统做出最优或满意的决策。本文概述了运筹学的主要特征和方法,简述了运筹学的发展历程,综述了运筹学几个主要分支的发展状况,介绍了运筹学中十几个有代表性的难题,展望了运筹学未来发展的方向。     

Multi-layer self-organizing polynomial neural networks and their development with the use of genetic algorithms

In this paper, we introduce a new architecture of genetic algorithms (GA)-based self-organizing polynomial neural networks (SOPNN) and discuss a comprehensive design methodology. Let us recall that the design of the “conventional” PNNs uses an extended group method of data handling (GMDH) and exploits polynomials (such as linear, quadratic, and modified quadratic functions) as well as considers a fixed number of input nodes (as being selected in advance by a network designer) at polynomial neurons (or nodes) located in each layer. The proposed GA-based SOPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional PNNs. The design procedure applied in the construction of each layer of a PNN deals with its structural optimization involving the selection of preferred nodes (or PNs) with specific local characteristics (such as the number of input variables, the order of the polynomial, and a collection of the specific subset of input variables) and addresses specific aspects of parametric optimization. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the network. To evaluate the performance of the GA-based SOPNN, the model is experimented with using chaotic time series data. A comparative analysis reveals that the proposed GA-based SOPNN exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.     

Improving the learning of Boolean queries by means of a multiobjective IQBE evolutionary algorithm

The Inductive Query By Example (IQBE) paradigm allows a system to automatically derive queries for a specific Information Retrieval System (IRS). Classic IRSs based on this paradigm [Smith, M., & Smith, M. (1997). The use of genetic programming to build Boolean queries for text retrieval through relevance feedback. Journal of Information Science, 23(6), 423–431] generate a single solution (Boolean query) in each run, that with the best fitness value, which is usually based on a weighted combination of the basic performance criteria, precision and recall.     

Using genetic algorithms to evolve a population of topical queries

Systems for searching the Web based on thematic contexts can be built on top of a conventional search engine and benefit from the huge amount of content as well as from the functionality available through the search engine interface. The quality of the material collected by such systems is highly dependant on the vocabulary used to generate the search queries. In this scenario, selecting good query terms can be seen as an optimization problem where the objective function to be optimized is based on the effectiveness of a query to retrieve relevant material. Some characteristics of this optimization problem are: (1) the high-dimensionality of the search space, where candidate solutions are queries and each term corresponds to a different dimension, (2) the existence of acceptable suboptimal solutions, (3) the possibility of finding multiple solutions, and in many cases (4) the quest for novelty. This article describes optimization techniques based on Genetic Algorithms to evolve “good query terms” in the context of a given topic. The proposed techniques place emphasis on searching for novel material that is related to the search context. We discuss the use of a mutation pool to allow the generation of queries with new terms, study the effect of different mutation rates on the exploration of query-space, and discuss the use of a especially developed fitness function that favors the construction of queries containing novel but related terms.